As lawmakers in Brasilia debated a controversial pension overhaul for
months, a robot more than 5,000 miles away in London kept a close eye
on all 513 of them. The algorithm, designed by technology startup Arkera
Inc., tracked their comments in Brazilian newspapers and government web
pages each day to predict the likelihood the bill would pass.

Weeks
before the legislation cleared its biggest obstacle in July, the
machine’s data crunching allowed Arkera analysts to predict the result
almost to the letter, giving hedge fund clients in New York and London
the insight to buy the Brazilian real near eight-month lows in May. It’s
since rallied more than 8%.

Lawmakers supporting the pension reform bill wave Brazilian flags in the lower house of the National Congress in Brasilia on July 10.Photographer: Andre Coelho/Bloomberg

This
is the kind of edge that a new generation of researchers are betting
will upend the research marketplace. For Arkera’s clients on Wall Street
and in the City of London, that means getting robots to filter through
the noise in faraway lands.

“There’s too many people to follow on Twitter, too many
websites, too many articles,” said Nav Gupta, the 48-year-old co-founder
of Arkera, which says its software can process as much information as 1,000 human analysts. “That’s a very expensive problem and everybody faces it.”

The company raised 4 million pounds ($4.9 million) last year from investors including Alan Howard of hedge fund Brevan Howard Asset Management LLP.

Using
so-called artificial intelligence to automate swathes of the research
process is quickly gaining traction because cost-conscious investment
banks are downsizing. In the U.K. alone, there was a 30% drop in
research budgets last year, Financial Conduct Authority data show. At
the 12 biggest banks, there’s been a 7% drop since 2015 in the number of
front-office staff covering currencies, such as traders and
researchers, according to London-based research analytics consultancy
Coalition Development Ltd.

That means it’s even harder than it used to be
to afford analysts on the ground in developing nations, about the only
places in the world where investors can get yield right now.

Data-science companies like Arkera and New York-based
Sigmoidal say they can solve this problem using machines that learn as
they go to dredge through tens of thousands of news articles, government
statements and social media accounts in languages as varied as Spanish,
Arabic and Chinese.

After an initial investment of up to $100,000, banks can save
$1 million over seven years using such systems because they don’t need
to hire as many data analysts, said Marek Bardonski, who was chief
executive officer of Sigmoidal when he spoke with Bloomberg in July. He
has since left the company. Previously, Bardonski, 27, was a computer
scientist at graphics chipmaker Nvidia.

Take this year’s
protests in Hong Kong. Bardonski said Sigmoidal’s software was able to
track developments in the Cantonese-language press and even identify the
non-verified Twitter feeds of protest leaders to monitor the risk of
further unrest. The technology is useful for far-away countries wracked
by political turmoil, places where investors are keen to put money but
don’t have easy access to information.

“The system can give an
edge over traditional analysts working for financial institutions,” said
Bardonski, who said typical reports will include charts on sentiment,
key word statistics and short written summaries. “Instead of getting
100,000 news articles, clients can get all the insights on one page.”

Neither
Sigmoidal nor Arkera would let Bloomberg see an example of an automated
report to see how readable it is compared with one produced by a human,
citing rules against sharing proprietary data.

But
the quality has gone downhill because mid-level analysts have left or
been pushed out, leaving junior analysts to do the work so their more
senior colleagues can go to client meetings. This is giving investors
even more impetus to seek out bespoke research, like paying cash to
speak with experts in the field or investing in automated research to support their senior fund managers and strategists.

“Asset managers now need to assess the value of every single
research service to assess if it’s worth paying for, how much they
should pay for it, and trying to filter the good from the bad,” Mahmud
said.

Vinit Sahni and Nav GuptaSource: Arkera

Under
MiFID II, asset managers must be prepared to demonstrate they’ve done
due diligence on all investments they make for their clients, something
that’s always been tricky in developing countries.

It was that very problem that inspired Gupta and his business
partner Vinit Sahni, whose careers spanned firms including Citadel LP,
DE Shaw & Co. and Goldman Sachs, to set up Arkera in 2015. During
their 20-year careers in investment banking, trying to find information
to substantiate something felt “like pulling teeth,” Sahni, 50, said.

So
the pair set up a team of data scientists and engineers to design a
search engine that investors can use to give them an edge in places like
Turkey, Mexico and Egypt. It works kind of like Google, only it’s
programmed to choose the most relevant sources from tens of thousands of articles, social media feeds and government releases.

As
good as robots are getting at deciphering market jargon, even their
developers admit they’ll never fully replace humans. In the next decade,
Sahni said smart machines will significantly enhance the capabilities
of human analysts.

“We will see advancements in cognitive
abilities, communication and the physical potential of humans as we
collaborate closely with machines and algorithms,” he said.